eig.py

import numpy as np
import sys
from numpy import linalg as LA

"""
This function takes square matrix and returns
the dominant eigin value and the smallest eigen value
and the value closest to a scalar k
"""
def solve(sq_matrix, k):
    if sq_matrix.shape[0] != sq_matrix.shape[1]:
        return

    vals, _ = LA.eig(sq_matrix)
    
    k_close_idx = np.argmin(np.abs(vals - k))

    return vals.max(), vals.min(), vals[k_close_idx]
    

if __name__ == '__main__':
    n = int(input('Please input the size of the matrix: '))

    elements = []
    
    print("Please input the elements of the matrix row by row:")
    print("Example:\n1 2 3 [Press enter]\n4 5 6 [Press enter]\n7 8 9 [Press enter]\n", "-" * 20)
    for i in range(n):
        line = input()

        for element in line.split():
            elements.append(float(element))
    
    if len(elements) != n*n:
        print(f"This is not a {n}x{n} matrix")
        sys.exit(1)
    
    k = float(input('Please input the value of k: '))
    
    print("Solution:", solve(np.array(elements).reshape(n,n), k))